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Predicting Home Exercise Adherence after Ischemic Stroke: Development and Validation of a Web-Based Nomogram.

Wenbo Li1, Qiujie Li1

  • 1Department of Clinical Nursing Education, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Brain and Behavior
|May 11, 2026
PubMed
Summary

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This study developed a web-based tool to predict poor adherence to home exercises after ischemic stroke. The nomogram identifies at-risk patients, aiding targeted interventions for better recovery.

Area of Science:

  • Rehabilitation Medicine
  • Digital Health
  • Predictive Analytics

Background:

  • Poor adherence to home-based functional exercises significantly impedes recovery post-ischemic stroke.
  • Identifying patients at risk of non-adherence is crucial for effective rehabilitation strategies.

Purpose of the Study:

  • To develop and validate a web-based predictive nomogram for identifying ischemic stroke patients at risk of poor adherence to home-based functional exercises.
  • To support timely and targeted interventions to improve patient outcomes.

Main Methods:

  • A cross-sectional study involving 536 ischemic stroke patients with limb dysfunction.
  • Latent profile analysis (LPA) to identify adherence patterns.
  • Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression to build the prediction model.
Keywords:
exercise therapynomogramspatient compliancestroke rehabilitation

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  • Internal validation using bootstrap resampling and assessment of discrimination, calibration, and clinical utility.

    • Main Results:

    • Three adherence profiles were identified: low (18.1%), moderate (42.2%), and high (39.7%).
    • Five independent predictors of poor adherence were identified: marital status, monthly income, caregiver status, knowledge level, and exercise motivation.
    • The prediction model demonstrated good discrimination (C-statistic = 0.848) and calibration, with positive clinical utility.

    Conclusions:

    • A robust prediction model combining LPA and LASSO-logistic regression was developed and validated.
    • The web-based nomogram facilitates early identification of patients at risk of poor adherence.
    • This tool can guide targeted interventions to enhance functional exercise adherence and improve stroke rehabilitation outcomes.